Artificial Neural Networks (ANN) with Keras in Python and R Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R
What you’ll learn
Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
Learn usage of Keras and Tensorflow libraries
Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
Building a Artificial Neural Networks (ANN) in Python and R
Use Artificial Neural Networks (ANN) to make predictions
Artificial Neural Networks (ANN) with Keras in Python and R Description
You’re looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?
You’ve found the right Neural Networks course!
After completing this course you will be able to:
Identify the business problem which can be solved using Neural network Models.
Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.